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Intro to Probability

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Basic teminology of probability

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Intro to ProbabilityVersión en línea

Basic teminology of probability

por Olena Hawranick
1

experiment empirical probability outcome event

In probability , the playing of the game is called an .

2

sample trials outcomes experiment space random

The are the possible results of an experiment , and the of an experiment contains every potential outcome that could occur in one trial of the experiment .

3

experiment trial

To find a probability , you need a repeatable procedure , or .

4

An A trial event

consists of a particular set of outcomes .

5

experiment outcomes outcomes event event trial space sample

For a coin - tossing experiment :
The is flipping the coin and observing which surface of the coin is visible . The possible are a head or a tail .
The would be S = { Head , Tail } .
If flipping a coin twice , an might be getting at least one head . This would involve the following set of three { HT , TH , HH } .

6

space random space uncertain certain outcome trial probability experiment sample sample empirical

A is defined as any activity or phenomenon that meets the following conditions .
1 . There is one distinct for each trial of the experiment .
2 . The outcome of the experiment is .
3 . The set of all distinct outcomes of the experiment can be specified and is called the , denoted by S .

7

classical probability empirical

The of an event ( E ) is obtained by performing a random experiment and computing the ratio of the number of outcomes in which a specified event occurs to the total number of trials .

8

small Classical Empirical probability large numbers

relies on the law of , which says that the greater the number of trials , the closer the empirical probability will be to the classical , or actual , probability .

9

classical small empirical numbers of probability probability law large

The states that if we continue to roll the die and record the results , the of rolling a particular number should slowly approach its .

10

Classical probability Empirical

can be measured as a simple proportion : the number of outcomes that compose the event divided by the number of outcomes in the sample space .

11

diagram venn table tree

When an experiment , like tossing a coin three times , is done in stages ( each coin toss could be considered a stage ) , a can be used to organize the outcomes in a systematic manner . The tree begins with the possible outcomes for the first stage and then branches extend from each of these outcomes for the possible outcomes in the second stage , and so on for each stage of the experiment . The sample space is found by following each branch of the tree to identify all the possible outcomes of the experiment .

12

1 1/2

The sum of the probabilities of all outcomes must equal . For example , we know that the sample space of a coin toss includes only two outcomes : heads and tails . The probability of the outcome of a single coin toss being heads is 1 / 2 . Thus , if we sum the probabilities of the two outcomes we have 1 / 2 + 1 / 2 = 1 .

13

happen certain never to

If an event has a probability of 1 , then the event is . This occurs when the event includes the entire sample space .

14

will never happen definitely

If an event has a probability of 0 , then the event . This occurs when the event is not in the sample space .

15

whole natural irrational rational real

The scale used to describe the probability of an event is always a number between 0 and 1 , inclusive . ( Note that the word inclusive means that 0 and 1 are included in the range of numbers . )

16

Empirical Theoretical

means based on , concerned with , or verifiable by observation or experience rather than theory or pure logic :

17

classical Empirical

probability is also called experimental probability .

18

Classical empirical

probability is also called theoretical probability .

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